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Rapid pathogen detection by metagenomic next-generation sequencing of infected body fluids

Nature Medicine volume 27, pages 115–124 (2021)Cite this article

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Abstract

We developed a metagenomic next-generation sequencing (mNGS) test using cell-free DNA from body fluids to identify pathogens. The performance of mNGS testing of 182 body fluids from 160 patients with acute illness was evaluated using two sequencing platforms in comparison to microbiological testing using culture, 16S bacterial PCR and/or 28S–internal transcribed ribosomal gene spacer (28S–ITS) fungal PCR. Test sensitivity and specificity of detection were 79 and 91% for bacteria and 91 and 89% for fungi, respectively, by Illumina sequencing; and 75 and 81% for bacteria and 91 and 100% for fungi, respectively, by nanopore sequencing. In a case series of 12 patients with culture/PCR-negative body fluids but for whom an infectious diagnosis was ultimately established, seven (58%) were mNGS positive. Real-time computational analysis enabled pathogen identification by nanopore sequencing in a median 50-min sequencing and 6-h sample-to-answer time. Rapid mNGS testing is a promising tool for diagnosis of unknown infections from body fluids.

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Fig. 1: Study workflow and sample distribution.
Fig. 2: Accuracy of mNGS testing and relative pathogen burden in body fluid samples.
Fig. 3: Comparison of mNGS and 16S (bacterial) or 28S–ITS (fungal) PCR.
Fig. 4: Comparison of relative pathogen burden in paired body fluid and plasma samples.

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Data availability

Metagenomic sequencing data (FASTQ files) after removal of human genomic reads have been deposited in the NCBI Sequence Read Archive (SRA) (PRJNA558701, under umbrella project PRJNA234047).

Code availability

SURPI+ v.1.0 (https://github.com/chiulab/SURPI-plus-dist) and SURPIrt v.1.0 software (https://github.com/chiulab/SURPIrt-dist) have been deposited on GitHub and are available for download for research use only. Linux (Ubuntu 16.04.6) and Python (python 2.7.12) scripts used for construction of dual-use Illumina and nanopore barcodes are provided in the Supplementary Information, ‘Pipeline for Designing 37mer Barcodes’. Other custom scripts for ROC curve and read length analysis have been deposited on Github (https://github.com/wei2gu/2020-NGSInfectedBodyFluids/).

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Acknowledgements

We thank H. Reyes, A. Chan, D. Ingebrigtsen, L. Dang, W. Lorizio, J. Streithorst, B. Fung and the UCSF clinical laboratory staff for assistance with orthogonal testing. We thank R. Da for assistance with sequencing runs. We thank S. Shiboski for biostatistics feedback. We thank members of the Chiu, Miller and DeRisi laboratories for feedback and support. This work was funded in part by an NIH grant (no. K08-CA230156) and a Burroughs-Wellcome Award to W.G., by Abbott Laboratories (C.Y.C.), a NIH/NHLBI grant (no. R01-HL105704, to C.Y.C.), a NIH/NIAID grant (no. R33-AI120977, to C.Y.C.), the California Initiative to Advance Precision Medicine (C.Y.C.), a UC Center for Accelerated Innovation grant funded by NIH grant U54HL119893 and NIH/NCATS UCSF-CTSI grant UL1TR000004 (C.Y.C.), and the Charles and Helen Schwab Foundation (C.Y.C.).

Author information

Author notes

  1. These authors contributed equally: Wei Gu, Xianding Deng, Marco Lee.

Authors and Affiliations

  1. Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, USA

    Wei Gu, Xianding Deng, Yasemin D. Sucu, Shaun Arevalo, Doug Stryke, Scot Federman, Allan Gopez, Kevin Reyes, Guixia Yu, Gurpreet Ishpuniani, Benjamin Briggs, Candace Wang, Elaine Hsu, Steve Miller & Charles Y. Chiu

  2. UCSF-Abbott Viral Diagnostics and Discovery Center, San Francisco, CA, USA

    Wei Gu, Xianding Deng, Yasemin D. Sucu, Shaun Arevalo, Doug Stryke, Scot Federman, Allan Gopez, Kevin Reyes, Guixia Yu, Gurpreet Ishpuniani, Benjamin Briggs, Candace Wang, Steve Miller & Charles Y. Chiu

  3. Leeds Teaching Hospitals NHS Trust, Leeds, UK

    Marco Lee

  4. Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA, USA

    Kelsey Zorn, Hannah Sample, Eric D. Chow & Joseph L. DeRisi

  5. Department of Medicine, University of California San Francisco, San Francisco, CA, USA

    Amy Berger

  6. Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA

    Michael R. Wilson

  7. Department of Neurology, University of California San Francisco, San Francisco, CA, USA

    Michael R. Wilson

  8. Chan Zuckerberg Biohub, San Francisco, CA, USA

    Joseph L. DeRisi

  9. Department of Medicine, Division of Infectious Diseases, University of California San Francisco, San Francisco, CA, USA

    Charles Y. Chiu

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  1. Wei Gu
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Contributions

W.G., X.D. and C.Y.C. conceived and designed the study. W.G., X.D. and C.Y.C. coordinated the study. W.G., X.D., M.L., Y.D.S., S.A., A.G., K.R., G.Y., B.B., E.D.C., C.W. and E.H. performed experiments. W.G., X.D., M.L., E.H. and C.Y.C. analyzed data. W.G., G.I., E.H. and C.Y.C. reviewed patient electronic medical records. S.F. and D.S. wrote software and performed SURPI bioinformatics analysis of mNGS data. K.Z., H.S. and G.I. enrolled patients in the study and assisted in patient data collection. A.B., M.R.W., S.M., J.L.D. and C.Y.C. provided clinical samples and resources. W.G., X.D., M.L. and C.Y.C. wrote and edited the manuscript. W.G. and C.Y.C. designed the figures. C.Y.C. supervised the study. All authors read the manuscript and agree to its contents.

Corresponding author

Correspondence to Charles Y. Chiu.

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Competing interests

C.Y.C. is the director of the UCSF-Abbott Viral Diagnostics and Discovery Center and receives research support funding from Abbott Laboratories, Inc. C.Y.C., D.S., S.F. and S.M. are inventors on a patent application on algorithms related to SURPI+ software (no. PCT/US/16/52912, ‘Pathogen Detection using Next-Generation Sequencing’). C.Y.C., X.D. and S.F. are inventors on a patent pending on algorithms related to SURPIrt software (Case no. SF2015-154, ’Methods for Real-Time Sequencing Analysis of Infectious Diseases’). Other coauthors declare no competing interests.

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Peer review information Alison Farrell is the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Metagenomic sequencing of body fluids.

a, Nanopore time to detection (minutes) across different body fluid types. Each data point represents the time to detection of the organism, if any, in each body fluid sample. b, Nanopore time to detection (minutes) in relation to pathogen DNA abundance in samples (reads per million, RPM). All box plots represent the median (centre), the interquartiles (minima and maxima), and 1.5 x interquartile range (whiskers). c, Precision-recall curves for Illumina and nanopore training fungal datasets. d, Precision-recall curves based on the Illumina training bacterial dataset in comparison with the composite standard. e, Precision-recall curves based on nanopore bacterial training datasets. f, Pie chart showing distribution of bacterial pathogen titers as estimated by semi-quantitative culture. g, Plot of nRPM values versus semi-quantitative bacterial titers. The nRPM corresponding to bacteria cultured in enrichment broth was significantly lower than the other higher-titer cultures (p = 0.006). h, Relative pathogen burden in positive and negative (non-infectious) body fluid samples. i, Log scale plot of the bacterium Achromobacter xylosoxidans from mNGS data, a common background contaminant in sequencing libraries. There is a log-linear relationship between the qPCR cycle threshold (Ct) value and the RPM corresponding to Achromobacter xylosoxidans. The background level of Achromobacter xylosoxidans is inversely correlated with the input concentration and is relatively constant.

Extended Data Fig. 2 ROC curves of mNGS test performance.

ROC curves are plotted from validation set data based on a clinical gold standard or composite standard. Data are presented as median true positive rates± the 95% confidence intervals. The 95% confidence interval was obtained via a bootstrap method with 2000 resampling iterations. a, Illumina dataset, bacterial detection (n = 127 samples). b, Nanopore dataset, bacterial detection (n = 43 samples), c, Illumina dataset, fungal detection (n = 127, 32 fungal organisms). d, Nanopore dataset, fungal detection (n = 43, 11 fungal organisms).

Extended Data Fig. 3 Relationship of external positive control organism titer with mNGS detection signal (expressed in nRPM).

Simple linear regression of normalized reads per million (nRPM) over four replicates per dilution factor, calculated as genome equivalents per mL (GE/mL) for a, Streptococcus uberis, b, Rhodobacter sphaeroides, c, Aspergillus oryzae, and d, Millerozyma farinosa.

Extended Data Fig. 4 Orthogonal testing for Case S31: Klebsiella pneumoniae infection of pleural fluid.

a, Genomic coverage of K. pneumoniae from Illumina mNGS. Sequencing spanned 36,490 base pairs, or 0.65% of the K. pneumoniae genome. b, Orthogonal confirmation of K. pneumoniae by dPCR of the sequencing library. Nine negative controls from other cases were run in parallel. Out of 10 sequencing libraries, only Case S31 had any positive droplets (n = 43 of 12022 total droplets as circled). c, Orthogonal confirmation of K. pneumoniae by dPCR of the DNA extract. Three positive droplets were detected, indicating a low positive result. d, Orthogonal confirmation of K. pneumoniae by dPCR of contralateral pleural fluid (sample C31). 29 and 24 positive droplets were detected out of 2 replicates. Digital PCR targeting Streptococcus mitis on both pleural fluids did not yield any positive droplets. The positive controls for these experiments were from sheared DNA from Klebsiella pneumoniae and Streptococcus mitis respectively, whereas the negative control was water. e, Sanger sequencing of the K. pneumoniae amplicon from dPCR. Shown are sequencing traces confirming the presence of K. pneumoniae.

Extended Data Fig. 5 Orthogonal testing for Cases S88: Klebsiella aerogenes from cerebrospinal fluid and S87: Bartonella henselae from a skin abscess.

a, Orthogonal confirmation of K. aerogenes by dPCR of the DNA extract. The sample was run in parallel with 9 negative controls. Out of 10 sequencing libraries, only Case S88 had positive dPCR droplets (n = 61). b, Genomic coverage of K. aerogenes from Illumina mNGS. The assembled genomic regions spanned 536,461 bp, or 9.9% of the bacterial genome. c, Orthogonal confirmation of Bartonella henselae by dPCR of the DNA extract. Positive dPCR droplets (n = 12) are seen in abscess fluid and the positive control consisting of sheared DNA from Bartonella henselae (ATCC 49882). The negative control was water.

Extended Data Fig. 6 Length distributions of pathogen cfDNA in mNGS data.

Analysis is performed on the 87 body fluid samples sequenced on both Illumina and nanopore platforms. a, Diagram showing how original genomic DNA lengths are recovered. Paired-end sequencing data is aligned to either a human or microbial genome, followed by determination of fragment length from the start and end positions and construction of a read length histogram. b, Histogram of average DNA lengths for human, bacterial, and fungal organisms obtained from mNGS data. Human DNA is observed to peak at the stereotypical 160 bp nucleosome footprint; both bacterial and fungal DNA are most abundant at sizes of <100 bp, but a higher molecular weight tail is observed extending to 500–600 bp. c, Histogram of bacterial read lengths according to sequencing platform. Illumina and nanopore sequencing platforms produce different size distributions. d, Length analysis of mNGS reads for samples with 16S PCR performed. Comparison of the length profiles of the 16S discordant bacteria cases (S31 and S88), 16S concordant bacteria cases (mean of S10, S36, S41, S69, S85, S128), and all bacteria mean. The pathogen cfDNA in cases S31 and S88 are more fragmented, with the vast majority of fragments <300 bp. The relative paucity of longer fragments could hinder 16S PCR amplification.

Extended Data Fig. 7 Comparison of different threshold variables on the training set to calibrate the thresholds for each variable used.

The final thresholds used are circled in each ROC chart. a, Comparison of different minimal read thresholds for bacteria calling. Based on this data and prior selection of minimal reads, we selected a minimal of 3 reads for the validation set. b, Comparison of using or not using a PCR Ct value normalization for bacteria calling. Normalization resulted in higher specificity and was used on the validation set. c, Comparison of using a same-genus/same-family filter to decrease an informatics artifact where a pathogen burden is high and related species would appear at significant lower values. Using this filter improved specificity. d, Comparison of different minimal read thresholds for fungal calling. We selected a minimal of 1 read based on the significantly higher sensitivity at the lowest threshold. e, Comparison of using or not using a PCR Ct value normalization for fungal calling. Normalization resulted in higher specificity and was used on the validation set.

Supplementary information

Supplementary Information

Supplementary Tables 1–13 and discussion.

Reporting Summary

Supplementary Table 1

Clinical characteristics and mNGS results of all cases used in the accuracy study.

Supplementary Table 2

Positive and negative external controls.

Supplementary Table 3

Differences between clinical and composite standards.

Supplementary Table 4

Bacterial false positives and false negatives using Illumina and nanopore sequencing.

Supplementary Table 5

Fungal true positives, false positives and false negatives using Illumina and nanopore sequencing.

Supplementary Table 11

PCR primers and indexes used for sequencing library barcoding.

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Gu, W., Deng, X., Lee, M. et al. Rapid pathogen detection by metagenomic next-generation sequencing of infected body fluids. Nat Med 27, 115–124 (2021). https://doi.org/10.1038/s41591-020-1105-z

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